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Proceedings Paper

Statistical signal processing using wavelet-domain hidden Markov models
Author(s): Matthew S. Crouse; Robert D. Nowak; Richard G. Baraniuk
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Paper Abstract

Most wavelet-based statistical signal and image processing techniques treat the wavelet coefficients as though they were statistically independent. This assumption is unrealistic; considering the statistical dependencies between wavelet coefficients can yield substantial performance improvements. In this paper, we develop a new framework for wavelet-based signal processing that employs hidden Markov models to characterize the dependencies between wavelet coefficients.

Paper Details

Date Published: 30 October 1997
PDF: 12 pages
Proc. SPIE 3169, Wavelet Applications in Signal and Image Processing V, (30 October 1997); doi: 10.1117/12.279689
Show Author Affiliations
Matthew S. Crouse, Rice Univ. (United States)
Robert D. Nowak, Michigan State Univ. (United States)
Richard G. Baraniuk, Rice Univ. (United States)

Published in SPIE Proceedings Vol. 3169:
Wavelet Applications in Signal and Image Processing V
Akram Aldroubi; Andrew F. Laine; Michael A. Unser, Editor(s)

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